Subject Details
Dept     : CSE
Sem      : 6
Regul    : R2019
Faculty : Dr.K.Sangeetha
phone  : NIL
E-mail  : sangithaprakash@gmail.com
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Syllabus

UNIT
1
BASICS OF NEURAL NETWORKS

Basic Concept of Neurons – Perceptron Algorithm – Single Layer Neural Network and Multilayer Neural Network- Feed Forward Neural Network and Back propagation Networks- Create and deploy neural networks using Tensor Flow for Image data.

UNIT
2
INTRODUCTION TO DEEP LEARNING

Deep Neural Networks – Gradient Descent – Differentiation Algorithms – Vanishing Gradient Problem – Mitigation – Rectified Linear Unit (ReLU) – Heuristics for Avoiding Bad Local Minima – Heuristics for Faster Training – Nestors Accelerated Gradient Descent

UNIT
3
CONVOLUTIONAL NEURAL NETWORKS

CNN Architectures – Convolution Layer– Pooling Layers –Hyper parameter–Activation Function–Recurrent and Recursive Nets – Recurrent Neural Networks – Deep Recurrent Networks – Recursive Neural Networks – Create and deploy Convolutional Neural networks using Keras for Image data.

UNIT
4
DEEP LEARNING ARCHITECTURES

Long Short Term Memory (LSTM) Networks – Sequence Prediction – Gated Recurrent – Encoder/Decoder Architectures – Autoencoders – Standard – Sparse – Denoising – Contractive – Variational Autoencoders – Applications of Autoencoders – Case Study: Representation Learning

UNIT
5
APPLICATIONS OF DEEP LEARNING

mages segmentation – Object Detection – Automatic Image Captioning – Image generation with Generative adversarial networks – Video to Text with LSTM models – Attention models for Computer Vision. Case Study: Named Entity Recognition – Opinion Mining using Recurrent Neural Networks – Parsing and Sentiment Analysis using Recursive Neural Networks

Reference Book:

1. Phil Kim, “Matlab Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence”, Apress, 2017. 2.Ragav Venkatesan, Baoxin Li, “Convolutional Neural Networks in Visual Computing”, CRC Press, 2018 3.Navin Kumar Manaswi, “Deep Learning with Applications Using Python”, Apress, 2018. 4.Joshua F. Wiley, “R Deep Learning Essentials”, Packt Publications, 2016.

Text Book:

1.Ian J. Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, MIT Press, 2017. 2.Francois Chollet, “Deep Learning with Python”, Manning Publications, 2018.